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        <title>RBG-AI: Benefits of Multilingual Language Models for Low-Resource Languages</title>
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            <title>Proceedings of the Tenth Conference on Machine Translation</title>
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    <abstract>This paper investigates how multilingual language models benefit low-resource languages through our submission to the WMT 2025 Low-Resource Indic Language Translation shared task. We explore whether languages from related families can effectively support translation for low-resource languages that were absent or underrepresented during model training. Using a quantized multilingual pretrained foundation model, we examine zero-shot translation capabilities and cross-lingual transfer effects across three language families: Tibeto-Burman, Indo-Aryan, and Austroasiatic. Our findings demonstrate that multilingual models failed to leverage linguistic similarities, particularly evidenced within the Tibeto-Burman family. The study provides insights into the practical feasibility of zero-shot translation for low-resource language settings and the role of language family relationships in multilingual model performance.</abstract>
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%0 Conference Proceedings
%T RBG-AI: Benefits of Multilingual Language Models for Low-Resource Languages
%A Hb, Barathi Ganesh
%A Ptaszynski, Michal
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Tenth Conference on Machine Translation
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-341-8
%F hb-ptaszynski-2025-rbg
%X This paper investigates how multilingual language models benefit low-resource languages through our submission to the WMT 2025 Low-Resource Indic Language Translation shared task. We explore whether languages from related families can effectively support translation for low-resource languages that were absent or underrepresented during model training. Using a quantized multilingual pretrained foundation model, we examine zero-shot translation capabilities and cross-lingual transfer effects across three language families: Tibeto-Burman, Indo-Aryan, and Austroasiatic. Our findings demonstrate that multilingual models failed to leverage linguistic similarities, particularly evidenced within the Tibeto-Burman family. The study provides insights into the practical feasibility of zero-shot translation for low-resource language settings and the role of language family relationships in multilingual model performance.
%U https://aclanthology.org/2025.wmt-1.100/
%P 1233-1239

Markdown (Informal)

[RBG-AI: Benefits of Multilingual Language Models for Low-Resource Languages](https://aclanthology.org/2025.wmt-1.100/) (Hb & Ptaszynski, WMT 2025)

• RBG-AI: Benefits of Multilingual Language Models for Low-Resource Languages (Hb & Ptaszynski, WMT 2025)

ACL

• Barathi Ganesh Hb and Michal Ptaszynski. 2025. RBG-AI: Benefits of Multilingual Language Models for Low-Resource Languages. In Proceedings of the Tenth Conference on Machine Translation, pages 1233–1239, Suzhou, China. Association for Computational Linguistics.